Neha Gyawali
4/26/2022
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Rows: 7246 Columns: 17
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (10): name, date, manner_of_death, armed, gender, race, city, state, thr...
## dbl (4): id, age, longitude, latitude
## lgl (3): signs_of_mental_illness, body_camera, is_geocoding_exact
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## New names:
## * `` -> ...9
## Rows: 25 Columns: 9
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): City
## dbl (1): Other
## lgl (1): ...9
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Here we can see the difference in the number of female versus male
fatalities. This helps us understand the demographic that is in danger
of being killed by a police officer.
When looking at this breakdown, we can tell that males are much more
likely to die from a police fatality than women.
## # A tibble: 7 x 2
## race count
## <chr> <int>
## 1 Asian 105
## 2 Black 1593
## 3 Hispanic 1088
## 4 Native American 91
## 5 Other 47
## 6 White 3022
## 7 <NA> 1300
These race breakdown charts show that the race breakdown of police fatalities are not proportional to the race breakdown of the United States as a whole. White people make up 60% of the population while they are only 20% of the fatalities. While Black people make up 13.4% of the population and are 21.9% of all fatalities. Hispanic people are
Some text about this table and how interesting the results are!
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
## Warning: Ignoring unknown aesthetics: text
Using these graphs we can see the top 25 cities with the most police fatalities. The first graph shows us the race breakdown in numbers and the second one shows us the race breakdown in percentage. We can see that in cities such as Chicago, Columbus, New York City, St.Louis, Atlanta, Louisville, and Philadelphia, Black people make up more than 50% of fatalities. Lets now look at the population breakdown of these cities to see they are proportional.
## # A tibble: 7,246 x 18
## # Groups: date [2,471]
## id name date manner_of_death armed age gender race city state
## <dbl> <chr> <date> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 3 Tim El~ 2015-01-02 shot gun 53 M Asian Shel~ WA
## 2 4 Lewis ~ 2015-01-02 shot gun 47 M White Aloha OR
## 3 5 John P~ 2015-01-03 shot and Taser~ unar~ 23 M Hisp~ Wich~ KS
## 4 8 Matthe~ 2015-01-04 shot toy ~ 32 M White San ~ CA
## 5 9 Michae~ 2015-01-04 shot nail~ 39 M Hisp~ Evans CO
## 6 11 Kennet~ 2015-01-04 shot gun 18 M White Guth~ OK
## 7 13 Kennet~ 2015-01-05 shot gun 22 M Hisp~ Chan~ AZ
## 8 15 Brock ~ 2015-01-06 shot gun 35 M White Assa~ KS
## 9 16 Autumn~ 2015-01-06 shot unar~ 34 F White Burl~ IA
## 10 17 Leslie~ 2015-01-06 shot toy ~ 47 M Black Knox~ PA
## # ... with 7,236 more rows, and 8 more variables:
## # signs_of_mental_illness <lgl>, threat_level <chr>, flee <chr>,
## # body_camera <lgl>, longitude <dbl>, latitude <dbl>,
## # is_geocoding_exact <lgl>, count <int>